In our approach, we leverage pre-trained GANs such as StyleGAN [1]} to provide rich and diverse priors for the task.
Unlike most GAN inversion methods, which also use pre-trained GANs, our method does not involve image-specific optimization at runtime. Once trained, the model only needs a single forward pass to upscale an image, which is more practical for applications that demand fast response.
The idea is partially inspired by the classic notion of dictionary [2]}. But unlike conventional approaches that construct a finite and imagery-derived dictionary, we exploit GAN as a more effective way for storing priors.
